Method, apparatus and program of predicting obstacle course

ABSTRACT

A method, an apparatus, and a program of predicting an obstacle course, capable of appropriately predicting a course of an obstacle even under a complicated traffic environment, are provided. The course, which the obstacle may take, is predicted based on the position and the internal state of the obstacle, and at the time of the prediction, a plurality of courses are probabilistically predicted for at least one obstacle. When there are a plurality of obstacles, the course in which different obstacles interfere with each other is obtained from the predicted courses, which a plurality of obstacles may take, and the predictive probability of the course for which the probabilistic prediction is performed from the courses in which they interfere with each other is lowered. Probability of realizing each of a plurality of courses including the course of which predicted probability is lowered is calculated.

This is a Continuation of application Ser. No. 12/312,195 filed Apr. 29,2009, which claims priority to PCT/JP2007/071923 filed Nov. 12, 2007,which claims priority to JP 2006-305744, filed Nov. 10, 2006. Thedisclosure of the prior applications is hereby incorporated by referenceherein in its entirety.

TECHNICAL FIELD

The present invention relates to a method, an apparatus, and a programof predicting an obstacle course, for predicting a course, which anobstacle present within a predetermined area may take.

BACKGROUND ART

Conventionally, as a technique to improve safety of a vehicle such as afour-wheeled vehicle, a technique to avoid collision by determining riskthat the vehicle collides with the obstacle with high accuracy is known.For example, the following patent document 1 discloses the techniquerelating to a collision avoidance apparatus provided with a yaw ratesensor for detecting yaw rate of a subject vehicle, a velocity detectiondevice for detecting a velocity of the subject vehicle, and a radardevice for detecting a position and a velocity of a surroundingobstacle.

In the conventional technique, a predicted travel trajectory of thesubject vehicle is obtained by the yaw rate sensor and the velocitysensor and a predetermined area on both sides of the predicted traveltrajectory is obtained as a predicted travel area of the subjectvehicle, and on the other hand, a predicted travel trajectory and apredicted travel area of the obstacle are obtained based on a positionand a velocity of the obstacle detected by the radar device. After that,a collision point or an adjacent point of the both is calculated fromeach predicted travel area of the subject vehicle and the obstacle.Also, collision risk is determined by calculating target decelerationand acceleration and target deceleration, and when the risk of collisionarises as a result of the determination, a velocity control of thesubject vehicle is performed according to the target deceleration andacceleration and the target deceleration.

Patent Document 1: Japanese Patent No. 2799375

DISCLOSURE OF THE INVENTION Problem to be Solved by the Invention

However since only the course of one obstacle is predicted for thesubject vehicle in the above-described conventional technique, thiscould not be applied to a complicated traffic environment where aplurality of obstacles is present around the subject vehicle.

The present invention is achieved in view of the above-describedcircumstances, and an object of the present invention is to provide themethod, the apparatus, and the program of predicting the obstacle coursefor appropriately predicting the course of the obstacle even under thecomplicated traffic environment.

Means for Solving Problem

To solve the problems as described above, an obstacle course predictionmethod according to the present invention, which a computer predicts acourse of the obstacle, the computer providing a memory unit thatmemorizes at least a position and an internal state of an obstaclepresent within a predetermined area from a mobile object, the methodincludes performing prediction of a course, which the obstacle may take,based on the position and the internal state of the obstacle read fromthe memory unit, and performing probabilistic prediction of a pluralityof courses, which the obstacle may take, for at least one of theobstacles at the time of the prediction; obtaining a course in which theobstacles interfere with each other within the courses, which aplurality of the obstacles predicted at the course predicting may take,and lowering predicted probability of the course for which theprobabilistic prediction is performed within the interfering courses,when there are a plurality of the obstacles; and calculating probabilityof realizing each of the plurality of courses including the course inwhich the predicted probability is lowered at the course interferenceassessing.

In the present invention, the obstacle course prediction methodaccording to the present invention further includes selecting one of theobstacles satisfying a predetermined condition as a specific obstacle,wherein the course predicting includes performing probabilisticprediction of a plurality of courses, which the specific obstacle maytake, and performing prediction of the course, which the obstacle otherthan the specific obstacle may take, the course interference assessinglowers predicted probability of a course of which distance at the sametime from a course, which the general obstacle may take, is smaller thana predetermined value, within a plurality of courses, which the specificobstacle may take, when there are a plurality of the obstacles, and theprobability calculating calculates probability of realizing each of aplurality of courses of the specific obstacle including the course thepredicted probability of which is lowered at the course interferenceassessing.

In the present invention, in the obstacle course prediction methodaccording to the present invention, the specific obstacle coursepredicting includes generating variation in position, which the specificobstacle may take with time, as a trajectory in space-time composed oftime and space, based on the position and the internal state of theobstacle, and performing probabilistic prediction calculation of acourse, which the specific obstacle may take, using the trajectorygenerated at the trajectory generating.

In the present invention, in the obstacle course prediction methodaccording to the present invention, the general obstacle coursepredicting predicts the course of the general obstacle assuming that theinternal state of the general obstacle is maintained.

In the present invention, the obstacle course prediction methodaccording to the present invention further includes generating a courseof the mobile object based on a position and an internal state of themobile object; calculating collision probability between the course ofthe mobile object generated at the course generating and each of aplurality of courses, which the specific obstacle may take, of whichprobability is calculated for each course at the probabilitycalculating.

In the present invention, in the obstacle course prediction methodaccording to the present invention, the course predicting performsprobabilistic prediction of a plurality of courses, which the obstaclemay take, the course interference assessing lowers probability of takingthe course of which distance at the same time from the course of anotherof the obstacles is smaller than a predetermined value within thecourses, which a plurality of the obstacles may take, when there are aplurality of the obstacles, and the probability calculating calculatesthe probability of realizing each of all the courses of a plurality ofthe obstacles including the course the predicted probability of which islowered at the course interference assessing.

In the present invention, in the obstacle course prediction methodaccording to the present invention, the course predicting includesgenerating variation in position, which the obstacle may take with time,as a trajectory in a space-time composed of time and space, based on theposition and the internal state of the obstacle, and performingprobabilistic prediction calculation of the course of the obstacle byusing the trajectory generated at the trajectory generating.

In the present invention, the obstacle course prediction methodaccording to the present invention further includes generating a courseof the mobile object based on the position and the internal state of themobile object; and calculating collision probability between the courseof the mobile object generated at the course generating and each of allcourses, which the obstacle may take, probability of which is calculatefor each course at the probability calculating.

An obstacle course prediction apparatus for predicting a course of anobstacle exiting around a mobile object according to the presentinvention, includes a memory unit that memorizes at least a position andan internal state of the obstacle present within a predetermined areafrom the mobile object; a course predicting unit that performsprediction of a course, which the obstacle may take, based on theposition and the internal state of the obstacle read from the memoryunit, and performs probabilistic prediction of a plurality of courses,which the obstacle may take, for at least one of the obstacles at thetime of the prediction; a course interference assessing unit thatobtains a course in which the obstacles interfere with each other withinthe courses, which a plurality of the obstacles predicted at the coursepredicting unit may take, and lowers predicted probability of the coursefor which the probabilistic prediction is performed within theinterfering courses, when there are a plurality of the obstacles; and aprobability calculating unit that calculates probability of realizingeach of the plurality of courses including the course in which thepredicted probability is lowered at the course interference assessingunit.

In the present invention, the obstacle course prediction apparatusaccording to the present invention further includes a specific obstacleselecting unit that selects one of the obstacles satisfying apredetermined condition as a specific obstacle, wherein the coursepredicting unit has a specific obstacle course predicting unit thatperforms probabilistic prediction of a plurality of courses, which thespecific obstacle may take, and a general obstacle course predictingunit that performs prediction of the course, which the obstacle otherthan the specific obstacle may take, the course interference assessingunit lowers predicted probability of a course of which distance at thesame time from a course, which the general obstacle may take, is smallerthan a determined value, within a plurality of courses, which thespecific obstacle may take, when there are a plurality of the obstacles,and the probability calculating unit calculates probability of realizingeach of a plurality of courses of the specific obstacle including thecourse the predicted probability of which is lowered at the courseinterference assessing unit.

In the present invention, in the obstacle course prediction apparatusaccording to the present invention, the specific obstacle coursepredicting unit includes a trajectory generating unit that generatesvariation in position, which the specific obstacle may take with time,as a trajectory in space-time composed of time and space, based on theposition and the internal state of the obstacle, and a predictioncalculating unit that performs probabilistic prediction calculation of acourse, which the specific obstacle may take, using the trajectorygenerated at the trajectory generating unit.

In the present invention, in the obstacle course prediction apparatusaccording to the present invention, the general obstacle coursepredicting unit predicts the course of the general obstacle assumingthat the internal state of the general obstacle is maintained.

In the present invention, the obstacle course prediction apparatusaccording to the present invention further includes a course generatingunit that generates a course of the mobile object based on a positionand an internal state of the mobile object; and a collision probabilitycalculating unit that calculates collision probability between thecourse of the mobile object generated at the course generating unit andeach of a plurality of courses, which the specific obstacle may take, ofwhich probability is calculated for each course at the probabilitycalculating unit.

In the present invention, in the obstacle course prediction apparatusaccording to the present invention, the course predicting unit performsprobabilistic prediction of a plurality of courses, which the obstaclemay take, the course interference assessing unit lowers probability oftaking the course of which distance at the same time from the course ofanother of the obstacles is smaller than a predetermined value withinthe courses, which a plurality of the obstacles may take, when there area plurality of the obstacles, and the probability calculating unitcalculates the probability of realizing each of all the courses of aplurality of the obstacles including the course the predictedprobability of which is lowered at the course interference assessingunit.

In the present invention, in the obstacle course prediction apparatusaccording to the present invention, the course prediction unit includesa trajectory generating unit that generates variation in position, whichthe obstacle may take with time, as a trajectory in a space-timecomposed of time and space, based on the position and the internal stateof the obstacle, and a prediction calculating unit that performsprobabilistic prediction calculation of the course of the obstacle byusing the trajectory generated at the trajectory generating unit.

In the present invention, the obstacle course prediction apparatusaccording to the present invention further includes a course generatingunit that generates a course of the mobile object based on the positionand the internal state of the mobile object; and a collision probabilitycalculating unit that calculates collision probability between thecourse of the mobile object generated at the course generating unit andeach of all courses, which the obstacle may take, probability of whichis calculate for each course at the probability calculating unit.

An obstacle course prediction program according to the present inventionallows the computer to execute the obstacle course prediction method.

Effect of the Invention

According to the present invention, even about the course, which may betaken with high probability when seen on a separate obstacle basis, ifthis interferes with another obstacle, the prediction may be performedby taking an effect thereof into consideration, by predicting thecourse, which the obstacle may take, based on the position and theinternal state of the obstacle, and at the time of the prediction,probabilistically predicting a plurality of courses of at least oneobstacle, and when there are a plurality of obstacles, obtaining thecourse in which different obstacles interfere with each other out of thepredicted courses, which a plurality of obstacles may take, and loweringthe predictive probability of the course for which the probabilisticprediction is performed out of the courses in which they interfere witheach other, and calculating probability that each of a plurality ofcourses including the course of which predicted probability is lowered,is realized. Therefore, it becomes possible to appropriately predict thecourse of the obstacle even under the complicated traffic environment.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to a first embodiment ofthe present invention;

FIG. 2 is a flowchart showing an overview of a process of an obstaclecourse prediction method according to the first embodiment of thepresent invention;

FIG. 3 is a view showing a circumstance around a subject vehicle afterextracting an obstacle;

FIG. 4 is a view showing an example of a probability distribution to beprovided to a course of a specific obstacle;

FIG. 5 is a view showing a curve obtained by removing a trajectory inwhich the specific obstacle collides with a general obstacle within apredetermined time period;

FIG. 6 is a view showing a probability distribution curve afternormalizing the curve in FIG. 5;

FIG. 7 is a view showing a display output example of a course predictedresult of the specific obstacle in a display unit;

FIG. 8 is a view showing an example of the probability distribution tobe provided to the general obstacle;

FIG. 9 is a view showing another display output example of a coursepredicted result of the specific obstacle in the display unit;

FIG. 10 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to a second embodiment ofthe present invention;

FIG. 11 is a flowchart showing an overview of a course predictionprocess of a specific obstacle in an obstacle course prediction methodaccording to the second embodiment of the present invention;

FIG. 12 is a flowchart showing a detail of a trajectory generationprocess of the specific obstacle;

FIG. 13 is a view schematically showing a trajectory of the specificobstacle;

FIG. 14 is a view schematically showing a trajectory set generated forthe specific obstacle in a three-dimensional space-time;

FIG. 15 is a view schematically showing a configuration example of aspace-time environment formed by adding a predicted course of thegeneral obstacle to the trajectory set of the specific obstacle;

FIG. 16 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to a third embodiment ofthe present invention;

FIG. 17 is a flowchart showing an overview of the process of an obstaclecourse prediction method according to the third embodiment of thepresent invention;

FIG. 18 is a view schematically showing a configuration example of thespace-time environment obtained by the trajectory generation process ofthe obstacle course prediction method according to the third embodimentof the present invention;

FIG. 19 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to a fourth embodiment ofthe present invention;

FIG. 20 is a flowchart showing an overview of a process of an obstaclecourse prediction method according to the fourth embodiment of thepresent invention;

FIG. 21 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to a fifth embodiment ofthe present invention;

FIG. 22 is a flowchart showing a detail of a collision probabilitycalculation process of an obstacle course prediction method according tothe fifth embodiment of the present invention; and

FIG. 23 is a view schematically showing a relationship between thetrajectory of the subject vehicle and a non-interference trajectory ofthe specific obstacle in the three-dimensional space-time.

EXPLANATIONS OF LETTERS OR NUMERALS

-   1, 11, 21, 31, 41 obstacle course prediction apparatus-   2, 32 Sensor unit-   3 Obstacle extracting unit-   4 Specific obstacle selecting unit-   5, 12 Specific obstacle-course predicting unit-   6 General obstacle-course predicting unit-   7 Course interference assessing unit-   8 Normalizing unit-   9 Output unit-   10 Memory unit-   13, 23 Trajectory generating unit-   14, 24 Prediction calculating unit-   22 Obstacle course predicting unit-   33 Subject vehicle-course generating unit-   34 Collision probability calculating unit-   35 Output unit-   91, 351 Image generating unit-   92, 352 Display unit-   131, 231 Operation selecting unit-   132, 232 Object operating unit-   133, 233 Determining unit-   353 Warning sound emitting unit

BEST MODE(S) FOR CARRYING OUT THE INVENTION

Hereinafter, a best mode for implementing the present invention(hereinafter, referred to as an “embodiment”) is described withreference to attached drawings.

First Embodiment

FIG. 1 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to a first embodiment ofthe present invention. An obstacle course prediction apparatus 1 shownin the drawing installed in a vehicle such as a four-wheeled vehicle,which is a mobile object, for predicting a course, which an obstaclepresent around a subject vehicle may take.

The obstacle course prediction apparatus 1 is provided with a sensorunit 2 for detecting a position and an internal state of an objectpresent within a predetermined area, an obstacle extracting unit 3 forextracting the obstacle included in the predetermined area based on aresult detected by the sensor unit 2, a specific obstacle selecting unit4 for selecting one obstacle (specific obstacle) satisfying apredetermine condition from the obstacles extracted by the obstacleextracting unit 3, a specific obstacle-course predicting unit 5 forprobabilistically predicting a course of the specific obstacle selectedby the obstacle selecting unit 4, a general obstacle-course predictingunit 6 for predicting a course of a general obstacle other than thespecific obstacle, a course interference assessing unit 7 for assessingan interference between a predicted course of the specific obstacle anda predicted course of the general obstacle, a normalizing unit 8, whichis probability calculating means for calculating probability that thecourse, which the specific obstacle may take, is realized, using anassessment result by the course interference assessing unit 7, an outputunit 9 for outputting information regarding the course of the specificobstacle calculated by the normalizing unit 8, and a memory unit 10 formemorizing information including the position and the internal state ofthe object detected by the sensor unit 2 and a variety of calculationresults.

The sensor unit 2 is realized by using millimeter wave radar, laserradar, an image sensor, or the like. In addition, the sensor unit 2 isprovided with various sensors such as a velocity sensor, a yaw ratesensor, an accelerated velocity sensor, and a rudder angle sensor.Meanwhile, the internal state of the object to be detected by the sensorunit 2 is a beneficial state, which may be used to predict the course ofthe object, and is preferably a physical amount such as a velocity(having a velocity and a direction) and a yaw rate (having a volume anda direction) of the object, and also includes a case in which values ofthe physical amounts are 0 (a state in which the object is stopped).

The output unit 9 has an image generating unit 91 for generating animage based on information corresponding to a result of the processperformed by the course interference assessing unit 7, and a displayunit 92, which is realized by using a liquid crystal display, a plasmadisplay, or an organic electroluminescence (EL) display, for displayingand outputting information including the image generated by the imagegenerating unit 91. Also, as the display unit 92, a projector is locatedon an upper posterior portion of a driver seat to make a superimposeddisplay on a front glass of the vehicle.

The memory unit 10 memorizes predicted results by the specificobstacle-course predicting unit 5 and the general obstacle-coursepredicting unit 6, and a result of the interference assessment by thecourse interference assessing unit 7, in addition to the detected resultby the sensor unit 2. The memory unit 10 is realized by using a ROM inwhich a program to activate a predetermined operation system (OS), anobstacle course prediction program according to the first embodiment, orthe like are memorized in advance, and a random access memory (RAM) inwhich a calculation parameter, data, or the like of each process arememorized. Also, the memory unit 10 may be realized by providing aninterface on which a computer-readable recording medium may be mountedon the obstacle course prediction apparatus 1 and mounting the recordingmedium corresponding to the interface.

The obstacle course prediction apparatus 1 having the above-describedfunctional configuration is an electronic device (computer) providedwith a central processing unit (CPU) having a calculation function and acontrol function. The CPU provided on the obstacle course predictionapparatus 1 executes a calculation process regarding an obstacle courseprediction method according to the first embodiment, by reading theinformation memorized and stored in the memory unit 10 and the variousprograms including the above-described obstacle course predictionprogram from the memory 10.

Meanwhile, the obstacle course prediction program according to the firstembodiment may be recorded in the computer-readable recording mediumsuch as a hard disk, a flexible disk, a CD-ROM, a DVD-ROM, a flashmemory, and a MO disk, and widely distributed.

Next, the obstacle course prediction method according to the firstembodiment is described. FIG. 2 is a flowchart showing an overview of aprocess of the obstacle course prediction method according to the firstembodiment. Although hereinafter it is described assuming that all theobjects to be predicted travel on a two-dimensional plane, the obstaclecourse prediction method according to the first embodiment is applicableto the object traveling in a three-dimensional space. Also, this isapplicable to a case in which one object has a plurality of degrees offreedom (an object such as a robot arm having six degrees of freedom,for example).

First, the sensor unit 2 detects the position of the object within thepredetermined area with respect to the subject vehicle and the internalstate thereof, and stores the detected information in the memory unit 10(step S1). Hereinafter, the position of the object shall be representedby a value of a central portion of the object, and the internal state ofthe object shall be specified by the velocity (velocity v, direction θ).

Next, the obstacle extracting unit 3 extracts the obstacle within thepredetermined area based on the result detected by the sensor unit 2(step S2). At the step S2, the object, which may be considered as theobstacle interrupting travel of the subject vehicle, is extracted fromthe objects detected at the step S1 and other objects are excluded. FIG.3 is a view showing a condition around an subject vehicle C₀ when twovehicles C₁ and C₂ are extracted as the obstacles with respect to thesubject vehicle C₀, which travels straight with a velocity v₀. Thisdrawing shows a case in which the subject vehicle C₀ travels on a leftlane L₁ of a road R having three lanes, and two preceding vehicles C₁and C₂ travel on a center lane L₂ on a right side thereof. In addition,there is no object at least within an area detectable by the sensor unit2 on a right lane L₃ on a rightmost side thereof. In FIG. 3, all threevehicles travel straight, and a velocity v₁ of a posterior vehicle C₁shall be larger than a velocity V₂ of an anterior vehicle C₂.

Subsequently, if there are a plurality of obstacles extracted by theobstacle extracting unit 3, the specific obstacle selecting unit 4selects one specific obstacle therefrom (step S3). A selection criterionwhen selecting the specific obstacle is set in advance, and theselection criterion may be selected from conditions of an object, whichis the closest to the subject vehicle C₀, an object, which is thefastest, or an object, which is the slowest, around the subject vehicleC₀. For example, in FIG. 3, when the obstacle, which is the closest tothe subject vehicle C₀, is made the specific obstacle, the vehicle C₁ isthe specific obstacle.

After that, the specific obstacle-course predicting unit 5probabilistically predicts a plurality of courses, which the specificobstacle selected at the step S3 may take (step S4). At the step S4,conventionally known various methods may be applied. For example, thecourse may be predicted by providing a predetermined probabilitydistribution to a plurality of courses, which the specific obstacle maytake, according to present status. Also, a model corresponding to a typeof the specific obstacle may be memorized in the memory unit 10 inadvance. When using the model, a corresponding model is read from thememory unit 10, and the course is probabilistically predicted by usingthe read model.

FIG. 4 is a view showing an example of the probability distribution tobe provided to the course of the specific obstacle at the step S4.Specifically, a case in which a probability distribution curve ρ₁ havingthe largest value in a straight through direction is provided to thespecific obstacle is shown. In this sense, an x coordinate in FIG. 4 isa coordinate in a width direction of the road R, and an origin thereofrepresents a present position of the specific obstacle. Meanwhile, theprobability distribution to be provided to the specific obstacle ispreferably unimodal as represented by a normal distribution; however, adistribution function thereof is not limited.

On the other hand, the general obstacle-course predicting unit 6predicts the course of the general obstacle from the present position(step S5). At that time, the general obstacle shall move while holdingthe internal state detected by the sensor unit 2 and memorized in thememory unit 10, and one course is predicted for one general obstacle.The step S5 is performed in parallel with the above-described specificobstacle course prediction process at the step S4.

Subsequently, the course interference assessing unit 7 assesses theinterference between a plurality of courses of the specific obstacle,which are probabilistically predicted at the step S4, and the course ofthe general obstacle predicted by the general obstacle-course predictingunit 6 at the step S5 (step S6). More specifically, the probability(predicted probability) that the specific obstacle takes the course tocollide with the course of the general obstacle out of a plurality ofcourses is set to 0 and this is removed. The collision at that time isan amount defined according to the type of the obstacle, and in a casein which both of the specific obstacle and the general obstacle arevehicles, it is determined that they collide with each other when adistance therebetween becomes smaller than a predetermined distance (forexample, standard width and length of the vehicle) at the same time.

FIG. 5 is a view showing a curve, which may be obtained by removing aline representing that the vehicle C₁ collides with the vehicle C₂,which is the general obstacle, in a predetermined time period, in a casein which the course probability distribution of the vehicle C₁, which isthe specific obstacle, is given as the probability distribution curve ρ₁shown in FIG. 4. In the curve ρ₁′ shown in this drawing, a total sum ofprobability is not 1, so that this does not provide the probabilitydistribution in a precise sense the way it is. Therefore, thenormalizing unit 8 obtains a course predicted probability of the vehicleC₁, which remains unremoved by the course interference assessing unit 7at the step S6 (step S7). That is to say, the normalizing unit 8normalizes such that the total sum of the probability of the course ofthe specific obstacle, which remains unremoved at the step S6, becomes1, and provides correctly defined probability to all the predictedcourses.

FIG. 6 is a view showing a probability distribution curve ρ₂ obtained bynormalizing the distribution ρ₁′ at the step S7. As is clear from FIG.6, under a road environment shown in FIG. 3, due to presence of thepreceding vehicle C₂, it is determined to be risky that the vehicle C₁catches up on the same while traveling straight through, and it isdetermined that there is no possibility that this travels straightthrough. On the other hand, in the conventional technique disclosed inthe above-described patent document 1, it is not possible to predict fora plurality of obstacles even under the road environment shown in FIG.3. In the conventional technique, if only the course of the vehicle C₁is predicted, it is determined that the probability of travelingstraight through is the highest. However, it is obvious that this courseis unrealistic due to the significantly high probability to collide withthe vehicle C₂. As is clear from this example, according to the obstaclecourse prediction method according to the first embodiment, the coursemay be predicted under a complicated traffic environment moreappropriately than in the conventional technique.

Next, the output unit 9 outputs predetermined information based on thepredicted course probability of the specific obstacle obtained at thestep S7 (step S8). For example, it is preferable to display an area inwhich the specific obstacle may take the course with probability largerthan a predetermined value. FIG. 7 is a view showing a display outputexample of a predicted result in the display unit 92, and schematicallyshowing the display output example of the course predicted result forthe vehicle C₁ as the specific obstacle in the road environment shown inFIG. 3. FIG. 7 shows a case in which an area D indicating a course ofwhich probability is not smaller than the predetermined value out of thepredicted courses of the vehicle C₁ is translucently superimposinglydisplayed on a front glass F of the subject vehicle C₀.

The above-described superimposed display is realized by projecting theimage generated by the image generating unit 91 on the front glass Ffrom the projector (not shown) installed on the upper posterior portionof the driver seat of the subject vehicle C₀. Thereby, a driver of thesubject vehicle C₀ may immediately recognize an area in which the riskmight arise in the near future while driving. Therefore, the driver mayappropriately avoid the risk by reflecting a recognition result todriving.

Meanwhile, when there is only one obstacle extracted by the obstacleextracting unit 3, this may be considered as the specific obstacle, andthe output unit 9 may output the predicted result by the specificobstacle-course predicting unit 5.

The obstacle course prediction method according to the first embodimentis repeated at predetermined time intervals, and information alwaysbased on the newest road environment is output. Therefore, according tothe obstacle course prediction method according to the first embodiment,it becomes possible to aid the driver of the subject vehicle to performthe appropriate operation in response to the road environment changingevery second.

According to the above-described first embodiment of the presentinvention, even about the course, which may be taken with highprobability when seen on a separate obstacle basis, if this interfereswith another obstacle, the prediction may be performed by taking aneffect thereof into consideration, by predicting the course, which theobstacle may take, based on the position and the internal state of theobstacle, and at the time of the prediction, probabilisticallypredicting a plurality of courses of the specific obstacles selected bya predetermined condition and predicting the course of the generalobstacle other than those, and when there are a plurality of obstacles,lowering the predicted probability of the course of which distance atthe same time from the course, which the general obstacle may take, issmaller than a predetermined value out of a plurality of courses, whichthe specific obstacle may take, and calculating the probability thateach of a plurality of courses of the specific obstacle including thecourse of which predicted probability is lowered, is realized.Therefore, it becomes possible to appropriately predict the course ofthe obstacle even under the complicated traffic environment.

In addition, according to the first embodiment, the obstacle, which ismost likely to be the obstacle for the vehicle, is selected as thespecific obstacle, and the probabilistic prediction is performed for aplurality of courses of the specific obstacle, and on the other hand,the course maintaining the present status is taken as the course ofother general obstacles, so that practical course prediction process ofthe obstacle may be realized without a heavy load on the device whilecontrolling a calculation amount required for the process.

Further, according to the first embodiment, it is possible to presentinformation including a degree of risk by outputting the resultpredicted for the specific obstacle. Therefore, the driver of thesubject vehicle, who receives the information, may drive while rapidlyand appropriately avoiding the risk, which might arise in the nearfuture while driving.

Although the general obstacle shall maintain the present status, and thegeneral obstacle-course predicting unit 6 generates one course in thefirst embodiment, it is also possible to predict the course assumingthat the general obstacle may also take a plurality of courses with apredetermined probability. In this case, it is preferable to adopt theprobability distribution, which is unimodal and has a spatial extentsmaller than that of the specific obstacle. FIG. 8 is a view showing anexample of the probability distribution to be provided to the generalobstacle when the probability distribution of the specific obstacle isρ₁ shown in FIG. 4. The probability distribution curve ρ_(G) shown inFIG. 8 shows a distribution of which dispersion is smaller than that ofthe probability distribution curve ρ₁ of the specific obstacle andpossibility of selecting the course in the straight through direction orin the vicinity thereof is significantly high. When using such aprobability distribution curve ρ_(G), a probability density functionρ₃(x) defined by the following equation (1) may be applied as theprobability distribution corresponding to a probability distributioncurve ρ₂ shown in FIG. 6.

[Equation 1]

ρ₃(x)=Cρ ₁(x){1−θ(ρ_(G)(x)−ε)}  (1)

wherein, C represents a normalization constant, θ(x) represents afunction satisfying an equation θ(x)=0(x<0), 1(x≥0), and ε represents apositive constant.

Also, the course predicted result of the specific obstacle may bedisplayed by allowing a display screen CN (refer to FIG. 7) of a carnavigation system installed in the subject vehicle C₀ to have a functionof the display unit 92. FIG. 9 is a screen showing the area D in whichit is predicted that the vehicle C₁, which is the specific obstacle,travels with the probability not smaller than the predetermined value inthis case.

Meanwhile, although the preceding vehicle traveling in the samedirection as the subject vehicle is described as the obstacle in theabove-described description, it is also possible to consider thefollowing vehicle traveling in the same direction as the subject vehicleas the obstacle. Also, it is possible that an opposing vehicle travelingin a direction opposed to the subject vehicle is considered as theobstacle. Further, it is possible that a stationary object is consideredas the obstacle.

Second Embodiment

A second embodiment of the present invention is characterized in that avariation in position, which the specific obstacle specified as in theabove-described first embodiment may take with time, is generated as atrajectory in space-time composed of time and space to perform thecourse prediction using the generated trajectory.

FIG. 10 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to the second embodiment.In an obstacle course prediction apparatus 11 shown in this drawing, theconfiguration is the same as that of the obstacle course predictionapparatus 1 according to the above-described first embodiment, exceptfor a specific obstacle-course predicting unit 12. Therefore, the samereference numerals are given to portions having the same functions asthose of the obstacle course prediction apparatus 1.

The specific obstacle-course predicting unit 12 has a trajectorygenerating unit 13 for generating the variation in position, which thespecific obstacle selected by the specific obstacle selecting unit 4 maytake with time, as the trajectory in the space-time composed of time andspace, and a predictive calculating unit 14 for performing theprobabilistic prediction calculation of the course of the specificobstacle by using the trajectory of the specific obstacle output fromthe trajectory generating unit 13.

The trajectory generating unit 13 is for predictively generating thetrajectory, which the specific obstacle may take until a predeterminedtime period has passed, and has an operation selecting unit 131 forselecting an operation to allow the specific obstacle to virtually moveon simulations from a plurality of operations, an object operating unit132 for performing the operation selected by the operation selectingunit 131 for a predetermined time period, and a determining unit 133 fordetermining whether the position and the internal state of the specificobstacle after being operated by the object operating unit 132 satisfypredetermined conditions or not.

Next, the obstacle course prediction method according to the secondembodiment of the present invention is described. The obstacle courseprediction method according to the second embodiment is the same as theobstacle course prediction method according to the above-described firstembodiment, except for the prediction process of the course of thespecific obstacle (refer to the flowchart in FIG. 2). Therefore, in thefollowing description, the course prediction process of the specificobstacle (corresponding to the step S4 in FIG. 2) is described indetail.

FIG. 11 is a flowchart showing an overview of the course predictionprocess of the specific obstacle. First, the trajectory generating unit13 generates a plurality of trajectories of the specific obstacle (stepS41). FIG. 12 is a flowchart showing a detail of the trajectorygeneration process by the trajectory generating unit 13. In FIG. 12,calculation to generate the trajectory for the specific obstacle O_(S)detected by the sensor unit 2 is performed N times (N is a naturalnumber). Also, time to generate the trajectory (trajectory generatingtime) is set to T(>0). By appropriately defining the trajectorygenerating time T (and an operation time Δt to be described later), itbecomes possible to perform a set of course prediction processes in apractical calculation time period.

The trajectory generating unit 13 first performs initialization in whicha value of a counter n indicating the number of trajectory generationfor the specific obstacle O_(S) (step S401)

Next, the trajectory generating unit 13 reads the result detected by thesensor unit 2 from the memory unit 10, and makes the read detectedresult an initial state (step S402). Specifically, a time t is set to 0,and an initial position (x(0), y(0)) and an initial internal state(v(0), θ(0)) of the specific obstacle O_(S) are made input information(x₃, y₀) and (v₀, θ₀) from the sensor unit 2, respectively.

Consecutively, the operation selecting unit 131 selects one operationfrom a plurality of selectable operations according to an operationselection probability provided to each operation in advance, as anoperation u(t) to be performed within a time period Δt thereafter (stepS403). An operation selection probability p(u_(c)) of selecting anoperation u_(c) is defined, for example, by associating an element ofoperation set {u_(c)} selectable as u(t) with a predetermined randomnumber. In this sense, the operation selection probability p(u_(c))different from each operation u_(c) may be provided, or equivalentprobability may be provided for all the elements of the operation set{u_(c)}. In the latter case, an equation p(u_(c))=1/(total number ofselectable operations) is satisfied. Meanwhile, it is also possible todefine the operation selection probability p(u_(c)) as a functiondependent on the position and the internal state of the specificobstacle O_(S) and an ambient road environment.

In general, the operation u_(c) is composed of a plurality of elements,and the contents of the selectable operation vary according to the typeof the specific obstacle O_(S). For example, when the specific obstacleO_(S) is the four-wheeled vehicle, an accelerated velocity and anangular velocity of the four-wheeled vehicle are defined by a steeringcontrol and a degree of pressure on an accelerator pedal. In view ofthis, the operation u_(c) performed for the specific obstacle O_(S),which is the four-wheeled vehicle, is defined by the element includingthe accelerated velocity and the angular velocity. On the other hand,when the specific obstacle O_(S) is a person, the operation u_(c) may bespecified by the velocity.

A more specific setting example of the operation u_(c) is as follows.When the specific obstacle O_(S) is the vehicle, the acceleration is ina range from −10 to +30 (km/h/sec) and a steering angel is in a rangefrom −7 to +7 (deg/sec) (directions thereof are specified by a sign),and when the specific obstacle O_(S) is the person, the velocity is in arange from 0 to 36 (km/h) and a direction is in a range from 0 to 360(deg). Meanwhile, the amounts described herein are continuous volumes.In such a case, the number of elements of each operation is made finiteby performing an appropriate discretization to compose the operation set{u_(c)}.

After that, the object operating unit 132 allows the operation u_(c)selected at the step S403 to be performed for the time period Δt (stepS404). Although the time period Δt is preferably smaller in terms ofaccuracy, this may be practically set to a value about 0.1 to 0.5 (sec).Meanwhile, in the following description, a trajectory generating time Tshall be integral multiplication of Δt; however, a value of T may bemade variable according to the velocity of the specific obstacle O_(S)and is not necessarily the integral multiplication of Δt.

Next, the determining unit 133 determines whether the internal state ofthe specific obstacle O_(S) after the operation u_(c) is performed atthe step S404 satisfies a predetermined control condition or not (stepS405). The control condition to be determined at the step S405 isdefined according to the type of the specific obstacle O_(S), and whenthe specific obstacle O_(S) is the four-wheeled vehicle, for example,this is defined by the area of the velocity after the operation at thestep S404 and the maximum vehicle G of the acceleration velocity afterthe operation at the step S404.

As a result of the determination at the step S405, when the internalstate of the specific obstacle O_(S) satisfies the predetermined controlcondition (Yes at the step S405), the determining unit 133 determineswhether the position of the specific obstacle O_(S) after the operationu_(c) is performed is within a movable area or not (step S406). Themovable area to be determined at the step S406 indicates the area suchas the road (including a roadway and a pathway). Hereinafter, a case inwhich the object is located within the movable area is represented as“to satisfy mobile condition”.

As a result of the determination at the step S406, when the specificobstacle O_(S) is located within the movable area (Yes at the stepS406), the trajectory generating unit 13 increments the time by Δt(t←t+Δt) to make the position and the internal state after the operationat the step S404 to (x(t), y(t)) and (v(t), θ(t)), respectively (stepS407).

Meanwhile, when there is any condition, which is not satisfied, at thesteps S405 and S406 (No at the step S405 or No at the step S406), theprocedure returns back to the step S402.

The above-described processes at the steps S402 to S407 are repeateduntil reaching the trajectory generating time T. That is to say, whenthe time t newly defined at the step S407 does not reach T (No at thestep S408), the procedure returns back to the step S403 to repeat theprocess. On the other hand, when the time t newly defined at the stepS407 reaches T (Yes at the step S408), the trajectory for the specificobstacle O_(S) is output and stored in the memory unit 10 (step S409).

FIG. 13 is a view schematically showing the trajectory of the specificobstacle O_(S) generated by repeating the set of processes from the stepS403 to the step S407 with the time t=0, Δt, 2Δt, . . . , T. Atrajectory P_(S) (m) (1≤m≤N, m is the natural number) shown in thisdrawing passes through a three-dimensional space-time (x, y, t) of twospace dimension (x,y) and one time dimension (t). The predicted courseof the specific obstacle O_(S) in the two-dimensional space (x,y) may beobtained by projecting the trajectory P_(S) (m) on an x-y plane.

After the step S409, when the value of the counter n does not reach N(No at the step S410), the trajectory generating unit 13 increments thevalue of the counter n by 1 (step S411), and the procedure returns backto the step S402 to repeat the above-described processes at the stepsS402 to S408 until reaching the trajectory generating time T.

When the counter n reaches N at the step S410 (Yes at the step S410),generation of all the trajectories for the specific obstacle O_(S) iscompleted. FIG. 14 is a view schematically showing the trajectory set{P_(S)(n)} composed of N trajectories P_(S)(1), P_(S)(2), . . . ,P_(S)(N) generated for the specific obstacle O_(S) in thethree-dimensional space-time. A starting point, that to say, an initialposition (x₀, y₀, 0) of each trajectory, which are elements of thetrajectory set {P_(S)(n)}, are the same (refer to the step S402).Meanwhile, FIG. 14 is absolutely the schematic view, and the value of Nmay be the value of several hundreds to several tens of thousands, forexample.

When the counter n reaches N at the step S410, this means that thetrajectory generation is completed, so that the trajectory generationprocess at the step S41 shown in FIG. 11 is finished.

In FIG. 14, density of presence probability of the specific obstacleO_(S) in each area of the space-time (hereinafter, referred to as“space-time probability density”) is provided as density per unit volumeof the trajectory set {P_(S)(n)} in each area of the space-time.Therefore, it becomes possible to obtain the probability that thespecific obstacle O_(S) passes through the predetermined area in thethree-dimensional space-time, by using the trajectory set {P_(S)(n)}composed of the trajectory generation process at the step S41.Meanwhile, the space-time probability density is absolutely aprobability concept in the space-time, so that the total sum of a valueof one object in the space-time is not necessarily 1.

When setting a specific value of the trajectory generation time T as afixed value in advance, this is preferably a value such that when thetrajectory is generated to a point larger than the value T, theprobability density distribution in space-time becomes uniform and thereis no meaning in calculating. For example, when the object is thefour-wheeled vehicle and when the four-wheeled vehicle normally travels,this may be set at most about T=5 (sec). In this case, when theoperation time period Δt at the step S404 is set to about 0.1 to 0.5(sec), a set of processes from the step S403 to the step S407 arerepeated 10 times to 50 times for generating one trajectory P_(S)(m).

Meanwhile, it is preferable to set the trajectory generation times Tdifferent from one type of the road to the next such as an expresshighway, a general road, and a two-track road, and to switch by a methodto read the type of the road now traveling from map data usingpositional data and a method to read the type of the road by a roadrecognition device applying image recognition or the like.

Also, it is preferable to statistically evaluate the probability densitydistribution in the space-time by using the trajectory calculated up tothe trajectory generation time T, and to adaptively control such as toreduce the trajectory generation time T when the distribution is uniformand to increase the generation time when the distribution is notuniform.

After the above-described trajectory generation process for the specificobstacle, the specific obstacle-course predicting unit 12probabilistically predicts the course, which the specific obstacle maytake (step S42). Although hereinafter, a case in which probability thatthe specified trajectory P_(S)(m) is selected from the trajectory set{P_(S)(n)} generated for the specific obstacle O_(S) is obtained as thespecific prediction calculation process in the prediction calculatingunit 14 is described; however, it goes without saying that theprediction calculation is merely one example.

When N trajectories of the specific obstacle O_(S) are generated,probability p(P_(S)(m)) that one trajectory P_(S)(m) out of them becomesan actual trajectory is calculated as follows. First, when an operationsequence {u_(m)(t)} to realize the trajectory P_(S)(m) of the specificobstacle is {u_(m)(0), u_(m)(Δt), u_(m)(2Δt), . . . , u_(m)(T)},probability that the operation u_(m)(t) is selected at the time t isp(u_(m)(t)), so that probability that the operation sequence {u_(m)(t)}is executed at t=0 to T may be obtained as

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 2} \right\rbrack & \; \\{{{{p\left( {u_{m}(0)} \right)} \cdot {p\left( {u_{m}\left( {\Delta \; t} \right)} \right)} \cdot {p\left( {u_{m}\left( {2\Delta \; t} \right)} \right)}}\mspace{14mu} \ldots \mspace{14mu} {p\left( {u_{m}(T)} \right)}} = {\prod\limits_{t = 0}^{T}\; {{p\left( {u_{m}(t)} \right)}.}}} & (2)\end{matrix}$

Therefore, when the N trajectory set {P_(S)(n)} is provided to thespecific obstacle O_(S), the probability p(P_(S)(m)) that one trajectoryP_(S)(m), which the specific obstacle O_(S) may take, is represented as

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 3} \right\rbrack & \; \\{{p\left( {P_{s}(m)} \right)} = {\frac{\prod\limits_{t = 0}^{T}\; {p\left( {u_{m}(t)} \right)}}{\sum\limits_{n = 1}^{N}\; \left( {\prod\limits_{t = 0}^{T}\; {p\left( {u_{n}(t)} \right)}} \right)}.}} & (3)\end{matrix}$

Herein, when all the operations u_(m)(t) are selected with equalprobability p₀ (wherein 0<p₀<1), the equation (2) becomes

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 4} \right\rbrack & \; \\{{\prod\limits_{t = 0}^{T}\; {p\left( {u_{m}(t)} \right)}} = {p_{0}^{h}.}} & (4)\end{matrix}$

Herein, a natural number h is a total number of the operation timeperiods Δt with t=0 to T, that is to say, the number of operations.Therefore, the total sum of the probability of the trajectory P_(s)(m)included in N trajectories, which the specific obstacle O_(s) may take,becomes Np₀ ^(h), and probability p(P_(s)(m)) that one trajectoryP_(s)(m) is selected out of them is obtained as

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 5} \right\rbrack & \; \\{{p\left( {P_{s}(m)} \right)} = \frac{1}{N}} & (5)\end{matrix}$

by substituting the equation (4) to the equation (3). That is to say,the probability p(P_(s)(m)) does riot depend on the trajectory P_(s)(m).

After that, the prediction calculating unit 14 obtains the presenceprobability of the specific obstacle O_(s) per unit volume in each areaof the three-dimensional space-time based on the calculated probabilityp(P_(s)(m)). The presence probability corresponds to the space-timeprobability density in the three-dimensional space-time of thetrajectory set {P_(s)(n)}, and this is generally large in the area inwhich density of the passing trajectories is high. The calculationresult in the prediction calculating unit 14 is output to the courseinterference assessing unit 7.

FIG. 15 is a view schematically showing a configuration example of thespace-time environment formed by adding the trajectory set {P_(S)(n)} ofthe specific obstacle O_(S) and the predicted course of the generalobstacle. The space-time environment Env(P_(S),P_(G)) shown in thisdrawing is composed of the trajectory set {P_(S)(n)} of the specificobstacle O_(S) (represented by a solid line) and one trajectory P_(G) ofthe general obstacle O_(G) (represented by a broken line).

More specifically, the space-time environment Env (P_(S),P_(G)) showsthe space-time environment in a case in which the general obstacle O_(G)in addition to the specific obstacle O_(S) travels on a flat and linearroad R such as the express highway in a +y axis direction (the subjectvehicle C₀ in which the obstacle course prediction apparatus 11 isinstalled is not included in the space-time environment). Herein, thetrajectory is independently generated for each obstacle withoutconsidering relationship between the obstacles, so that the trajectoriesof different objects might intersect in the space-time.

The process thereafter is performed as in the above-described firstembodiment. That is to say, the trajectory, which intersects with thetrajectory P_(G) of the general obstacle O_(G), that is to say, thetrajectory on which the specific obstacle O_(S) and the general obstacleO_(G) collide with each other is removed from the trajectory set{P_(S)(n)} of the specific obstacle O_(S), as the interferenceassessment process between the specific obstacle O_(S) and the generalobstacle O_(G) (step S6). The collision at that time is defined as inthe above-described first embodiment, and includes a case in which thetwo trajectories get closer to each other with a distance smaller than apredetermined distance according to the type of the obstacle, inaddition to a case in which the two trajectories merely haveintersection.

After that, the normalizing unit 8 normalizes such that the total sum ofthe probability of each element of the trajectory set remainingunremoved (non-interference trajectory set) {P_(S)′ (n)} becomes 1 tocalculate the probability (step S7). Next, the output unit 9 outputs theinformation based on the probability distribution obtained at the stepS7 (step S8).

According to the above-described second embodiment of the presentinvention, even about the course, which may be taken with highprobability when seen on a separate obstacle basis, if this interfereswith another obstacle, the prediction may be performed by taking aneffect thereof into consideration, by predicting the course, which theobstacle may take, based on the position and the internal state of theobstacle, and at the time of the prediction, probabilisticallypredicting a plurality of courses of the specific obstacles selected bya predetermined condition and predicting the course of the generalobstacle other than those, and when there are a plurality of obstacles,lowering the predicted probability of the course of which distance atthe same time from the course, which the general obstacle may take, issmaller than a predetermined value out of a plurality of courses, whichthe specific obstacle may take, and calculating the probability thateach of a plurality of courses of the specific obstacle including thecourse of which predicted probability is lowered, is realized.Therefore, as in the above-described first embodiment, it becomespossible to appropriately predict the course of the obstacle even underthe complicated traffic environment.

Also, according to the second embodiment, by generating the variation inposition, which the specific obstacle may take with time, as thetrajectory in the space-time composed of time and space, and byprobabilistically predicting the course of the specific obstacle byusing the generated trajectory, the course of a dynamic object may bepredicted with high accuracy.

Meanwhile, in the second embodiment, when performing the trajectorygeneration process of the specific obstacle in the space-time, thetrajectory may be generated by allowing all the selectable operations tobe performed. An algorithm realizing such trajectory generation processmay be realized by applying a recursive call by depth-first search orbreadth-first search, for example. In this case, the number of elements,that is to say, the number of trajectories of the trajectory set{P_(S)(n)} of the specific obstacle O_(S), is not known until thetrajectory generation process for the specific obstacle O_(S) isfinished. Therefore, when generating the trajectory, which each objectmay take, by fully searching the executable operations, the searchmethod having the optimal calculation amount may be selected accordingto the number of elements of the operation u_(c)(t) at the operationtime period Δt (degree of discretization when the operation u_(c)(t) isa continuous amount).

In addition, the second embodiment is applicable to a four-dimensionalspace-time (three space dimensions and one time dimension) as in a caseof applying to the vehicle traveling on the road with vertical interval.

Third Embodiment

A third embodiment of the present invention is different from theabove-described two embodiments, and is characterized in equallytreating all the obstacles, generating the variation in position, whicheach obstacle may take with time, as the trajectory in the space-time,and predicting the course of the obstacle using the generatedtrajectory.

FIG. 16 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to the third embodimentof the present invention. An obstacle course prediction apparatus 21shown in this drawing has the sensor unit 2, the obstacle extractingunit 3, an obstacle course predicting unit 22 for performing probabilityprediction of the course of the obstacle extracted by the obstacleextracting unit 3, the course interference assessing unit 7, whichassesses interference of the predicted course between the obstaclespredicted by the obstacle course predicting unit 22, the normalizingunit 8, the output unit 9, and the memory unit 10. The output unit 9 hasthe image generating unit 91 and the display unit 92.

The obstacle course predicting unit 22 has a trajectory generating unit23 for generating the variation in position, which the obstacleextracted by the obstacle extracting unit 3 may take with time, as thetrajectory in space-time, and a prediction calculating unit 24 forprobabilistically performing a prediction calculation of the course ofeach obstacle by using the trajectories of a plurality of obstaclesoutput from the trajectory generating unit 23. Out of them, thetrajectory generating unit 23 is for predictably generating thetrajectory, which the object may take before a predetermined time periodhas passed, and has an operation selecting unit 231, an object operatingunit 232, and a determining unit 233, just as the trajectory generatingunit 13 described in the above-described second embodiment.

The obstacle course predicting unit 22 predicts the courses of all theobstacles by equally treating a plurality of obstacles. Also, the courseinterference assessing unit 7 performs the interference assessment byremoving all the courses, which interfere with each other, that is tosay, the trajectories, which get closer to each other with a distancesmaller than a predetermined distance at the same time, from thepredicted courses.

Next, the obstacle course prediction method according to the thirdembodiment is described with reference to a flowchart shown in FIG. 17.First, the sensor unit 2 detects a position of an object within apredetermined area with respect o the subject vehicle and an internalstate thereof, and stores the detected information in the memory unit 10(step S11). In the third embodiment also, the position of the object isrepresented by the value of the central portion of the object, and theinternal state of the object is specified by the velocity (velocity vand direction θ).

After that, the obstacle extracting unit 3 extracts the obstacle withinthe predetermined area, based on the result detected by the sensor unit2 (step S12).

Next, the obstacle course predicting unit 22 probabilistically predictsthe courses, which a plurality of obstacles extracted by the obstacleextracting unit 3, may take (step S13). A specific course predictionprocess to be performed for each obstacle at the step S13 is similar tothe course prediction process of the specific obstacle in theabove-described second embodiment (refer to FIGS. 11 and 12). In thefollowing description, a total number of the obstacles detected by thesensor unit 2 is set to 1, and the calculation to generate thetrajectory shall be performed N_(i) times for one obstacle O_(i) (1≤i≤I,i is a natural number) (I and N_(i) are natural numbers).

The trajectory generating unit 23 reads the result detected by thesensor unit 2 from the memory unit 10, and sets the read detected resultas an initial state. Thereafter, the operation selecting unit 231selects an operation u_(i) (t) to be performed within the time period Δtafter the initial state. At that time, the operation selecting unit 231selects one operation out of a plurality of selectable operationsaccording to the operation selection probability provided to eachoperation in advance. Meanwhile, a specific operation u_(ic) is set justas the operation u_(c) in the above-described second embodiment. Also,an operation selection probability p(u_(ic)) to select the operationu_(ic) also is defined as the above-described operation selectionprobability p(u_(c)).

After that, the object operating unit 232 allows the operation u_(ic)selected by the operation selecting unit 231 to be performed for thetime period Δt, and the determining unit 233 determines whether theinternal state of the object O_(i) after the operation u_(ic) isperformed satisfies a predetermined control condition or not anddetermines whether the position of the object O_(i) after the operationu_(ic) is performed is within the movable area or not. The trajectorygenerating unit 23 increments the time by Δt (t←t+Δt) only when theposition and the internal state of the object O_(i) after the operationu_(ic) is finished satisfy all the conditions, as a result ofdetermination at the determining unit 233, to set the position and theinternal state after the operation to (x_(i)(t), y_(i)(t)) and(v_(i)(t), θ_(i)(t)), respectively. By repeating the process N_(i)times, the process for one obstacle O_(i) is finished. After that, thesimilar process is performed for another obstacle O_(i).

The trajectory generating unit 23 performs the trajectory generationprocess for all the obstacles, thereby the space-time environmentcomposed of trajectory set, which a plurality of objects present withinthe predetermined area in the three-dimensional space-time may take, isformed. FIG. 18 is a view schematically showing a configuration exampleof the space-time environment. The space-time environment Env (P₁, P₂)shown in the drawing is formed of the trajectory set {P₁(n)} of theobstacle O₁ (indicated by a solid line in FIG. 18) and the trajectoryset {P₂(n)} of the obstacle O₂ (indicated by a broken line in FIG. 18).More specifically, the space-time environment Env (P₁, P₂) shows thespace-time environment when two obstacles O₁ and O₂ travel on the flatand linear road R such as the express highway in the +y axis direction.In the third embodiment, the trajectory is generated for each objectwithout considering the correlation between the objects, so that thetrajectories of different objects might intersect with each other inspace-time.

In FIG. 18, the space-time probability density of the obstacle O_(i) ineach area in space-time is provided as density per unit volume of thetrajectory set {P_(i)(n)} (i=1, 2) in each area in space-time.Therefore, in the second embodiment, it is possible to obtain theprobability that the obstacle O_(i) passes through a predetermined areain the three-dimensional space-time.

Next, the prediction calculating unit 24 probabilistically predicts thecourse, which each obstacle may take. The prediction calculation foreach obstacle at that time is substantially the same as that at the stepS42 in the above-described second embodiment. Therefore, when thetrajectory set {P_(i)(n)} is provided, the probability p(P_(i)(m)) thatone trajectory P_(i) (m) belonging to this set is selected isrepresented as

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 6} \right\rbrack & \; \\{{p\left( {P_{i}(m)} \right)} = {\frac{\prod\limits_{t = 0}^{T}\; {p\left( {u_{m}(t)} \right)}}{\sum\limits_{n = 1}^{N}\; \left( {\prod\limits_{t = 0}^{T}\; {p\left( {u_{n}(t)} \right)}} \right)}.}} & (6)\end{matrix}$

Herein, when all the operations u_(im)(t) are selected with equalprobability p₀ (0<p₀<1), the probability p(P_(i)(m)) that one trajectoryp_(i)(m) is selected is represented as

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 7} \right\rbrack & \; \\{{{p\left( {P_{i}(m)} \right)} = \frac{1}{N_{i}}},} & (7)\end{matrix}$

and the probability p(P_(i)(m)) does not depend on the trajectoryp_(i)(m). Meanwhile, in the equation (7), when the numbers oftrajectories to be generated for all the objects are the same (N), sinceN₁=N₂= . . . =N_(I)=N (constant), p(P_(i)(m))=1/N, and this is constantregardless of the obstacle O_(i). Therefore, in this case, it becomespossible to simplify the prediction calculation in the predictioncalculating it 24 to execute a predetermined prediction calculation morerapidly, by normalizing the value of the probability p(P_(i)(m)) to 1.

After that, the prediction calculating unit 24 obtains the presenceprobability of the obstacle O_(i) per unit volume in each area in thethree-dimensional space-time, based on the probability p(P_(i)(m))calculated for each obstacle O_(i) (i=1, 2, . . . , I). The presenceprobability corresponds to the space-time probability density of thetrajectory set {P_(i)(n)} in three-dimensional space-time, and thepresence probability is generally high in the area in which density ofpassing trajectories is high.

After the above-described obstacle course prediction process at the stepS13, the course interference assessing unit 7 performs the interferenceassessment between the obstacles (step S14). When performing the courseinterference assessment, the trajectories intersecting with each otherare removed from the trajectory set {P₁(n)}, {P₂(n)}, . . . , {P_(I)(n)}for all the obstacles. Meanwhile, in the third embodiment also, thedefinition of the intersection of the trajectories is the same as thatof the above-described second embodiment.

After that, the courses of all the obstacles remaining unremoved arecalculated as the course predicted probability of all the obstaclesunder the space-time environment (step S15). At that time, thenormalizing unit 8 normalizes such that the total sum of theprobabilities of all the courses remaining unremoved becomes 1.

Finally, the output unit 9 outputs predetermined information based onthe predicted course probabilities of all the obstacles calculated atthe step 15 (step 16). For example, it is preferable that the area inwhich the course is taken probability larger than the predetermine valueis displayed. The display method on the display unit 92 in this case maybe the superimposed display on the front glass, or may be the display onthe display screen CN of the car navigation system, described in theabove-described first embodiment.

Meanwhile, the obstacle course prediction method according to the thirdembodiment is applicable to the four-dimensional space-time (three spacedimensions and one time dimension) as in the above-described secondembodiment.

According to the above-described third embodiment of the presentinvention, even about the course, which may be taken with highprobability when seen on a separate obstacle basis, if this interfereswith another obstacle, the prediction may be performed by taking aneffect thereof into consideration, by predicting the course, which theobstacle may take, based on the position and the internal state of theobstacle, and at the time of the prediction, probabilisticallypredicting a plurality of courses of the obstacles, and when there are aplurality of obstacles, lowering the probability of taking the course ofwhich distance from the course of the different obstacle at the sametime is smaller than a predetermined value out of the courses, which aplurality of obstacles may take, and calculating the probability thateach of all the courses of a plurality of obstacle including the courseof which predicted probability is lowered, is realized. Therefore, itbecomes possible to appropriately predict the course of the obstacleeven under the complicated traffic environment.

Also, according to the third embodiment, the course of the dynamicobject may be predicted with high accuracy by generating the variationin position, which the obstacle may take with time, as a plurality oftrajectories in space-time composed of time and space andprobabilistically predicting the course, which all the obstacles maytake, by using a plurality of generated trajectories.

Meanwhile, when predicting the course of the obstacle in the thirdembodiment, any of conventionally known probabilistic method may beapplied.

Fourth Embodiment

A fourth embodiment of the present invention is characterized ingenerating a course of the subject vehicle in addition to predicting thecourse of the specific obstacle as in the above-described firstembodiment, and calculating collision probability of the specificobstacle and the subject vehicle.

FIG. 19 is a block diagram showing a functional configuration of theobstacle course prediction apparatus according to the fourth embodiment.An obstacle course prediction apparatus 31 shown in this drawing detectsthe position and the internal state of the object present within thepredetermined area, and has a sensor unit 32 for detecting the positionand the internal state of the subject vehicle, a subject vehicle-coursegenerating unit 33 for generating the course of the subject vehicle fromthe present position, a collision probability calculating unit 34 forcalculating the collision probability of the subject vehicle and thespecific obstacle, and an output unit 35 for outputting informationregarding the collision probability of the subject vehicle and thespecific obstacle calculated by the collision probability calculatingunit 34. The output unit has an image generating unit 351 for generatingan image based on the collision probability calculated by the collisionprobability calculating unit 34, a display unit 352 for displayoutputting information including the image generated by the imagegenerating unit 351, and a warning sound emitting unit 353 for emittinga warning sound (including voice) when the collision probability of thesubject vehicle course generated by the own course generating unit 33and the specific obstacle is larger than a predetermined thresholdvalue. A configuration of the obstacle course prediction apparatus 31other than that described herein is the same as the configuration of theobstacle course prediction apparatus 1 according to the above-describedfirst e embodiment (refer to FIG. 1).

FIG. 20 is a flowchart showing an overview of the process of theobstacle course prediction method according to the fourth embodiment.First, the position of the object within the predetermined area withrespect to the subject vehicle C₀ and the internal state thereof and theposition and the internal state of the subject vehicle are detected, andthe detected information is stored in the memory unit 10 (step S21).

Next, the obstacle extracting unit 3 extracts the obstacle within thepredetermined area, based on the result detected by the sensor unit 32(step S22).

Subsequently, the specific obstacle selecting unit 4 selects onespecific obstacle out of the obstacles extracted by the obstacleextracting unit 3 (step S23). In the fourth embodiment, since the sensorunit 32 may also detect the internal state of the subject vehicle C₀,the obstacle of which time to collision (TTC), which is the time untilthe subject vehicle C₀ collides with the obstacle when traveling underthe present circumstances, is the shortest may be selected as thespecific obstacle.

After that, the processes from the step S24 to the step S27 to beperformed for the specific obstacle O_(S) and the general obstacle O_(G)other than the subject vehicle C₀ are the same as the processes from thestep S4 to the step S7 described in the above-described firstembodiment.

On the other hand, the subject vehicle-course generating unit 38generates the course of the subject vehicle using the subject vehicleinformation detected by the sensor unit 2 (step S28). Specifically, thesubject vehicle-course generating unit 33 generates the trajectory in acase in which the subject vehicle travels under the presentcircumstances. Meanwhile, if it is possible that the sensor unit 32detects a road surface environment such as a white line, the trajectoryaccording to the number of travelable lanes may be generated. The stepS28 is performed in parallel with the processes from the step S22 to thestep S27.

After that, the collision probability calculating unit 34 calculates theprobability that the course of the subject vehicle generated by thesubject vehicle-course generating unit 33 and the course of the specificobstacle obtained by the course interference assessing unit 7 collidewith each other (step S29). At the step S29, when a distance between thecourse of the subject vehicle and the course of the specific obstaclebecomes smaller than a predetermined distance at the same time, they areconsidered to collide with each other to calculate the collisionprobability is calculated. The distance with which they are consideredto collide with each other is defined according to the type of thespecific obstacle.

The output unit 35 outputs predetermined information based on thecollision probability obtained at the step S29 (step S30). For example,this displays with the display unit 352 when the collision probabilityis larger than the predetermined threshold value, and emits the warningsound from the warning sound emitting unit 35 when the collisionprobability is larger than the predetermined threshold value. Meanwhile,when the subject vehicle-course generating unit 33 generates a pluralityof courses, the course (or lane) of which collision probability is thelowest may be displayed or informed by voice as suggested course (or asuggested lane).

According to the above-described fourth embodiment of the presentinvention, even about the course, which may be taken with highprobability when seen on a separate obstacle basis, if this interfereswith another obstacle, the prediction may be performed by taking aneffect thereof into consideration, by predicting the course, which theobstacle may take, based on the position and the internal state of theobstacle, and at the time of the prediction, probabilisticallypredicting a plurality of courses of the specific obstacles selected bya predetermined condition and predicting the course of the generalobstacle other than those, and when there are a plurality of obstacles,lowering the predicted probability of the course of which distance atthe same time from the course, which the general obstacle may take, issmaller than a predetermined value out of a plurality of courses, whichthe specific obstacle may take, and calculating the probability thateach of a plurality of courses of the specific obstacle including thecourse of which predicted probability is lowered, is realized.Therefore, it becomes possible to appropriately predict the course ofthe obstacle even under the complicated traffic environment.

Also, according to the fourth embodiment, it becomes possible toappropriately determine safety of the subject vehicle course under thecomplicated traffic environment within the practical time period, bycalculating the collision probability between the subject vehicle courseand the predicted course of the specific obstacle.

Meanwhile, as a modification of the fourth embodiment, it is alsopossible to further provide an subject vehicle-course generating unitand a collision probability calculating unit to the obstacle courseprediction apparatus according to the third embodiment. In this case,the probabilities for the courses of all the obstacles are calculatedand the collision probability with the subject vehicle course iscalculated.

Fifth Embodiment

A fifth embodiment of the present invention is characterized ingenerating the course of the subject vehicle in addition to predictingthe course of the specific obstacle by using the trajectory generated inthe three-dimensional space-time as in the above-described secondembodiment, thereby obtaining the collision probability between thespecific obstacle and the subject vehicle.

FIG. 21 is a block diagram showing a functional configuration of anobstacle course prediction apparatus according to the fifth embodimentof the present invention. An obstacle course prediction apparatus 41shown in this drawing detects the position and the internal state of theobject present within the predetermined area, and is provided, with thesensor unit 32 for detecting the position and the internal state of thesubject vehicle, the subject vehicle-course generating unit 33 forgenerating the course of the subject vehicle from the present position,the collision probability calculating unit 34 for calculating thecollision probability between the subject vehicle and the specificobstacle, and the output unit 35 for outputting the informationregarding the collision probability between the subject vehicle and thespecific obstacle calculated by the collision probability calculatingunit 34. The output unit 35 has the image generating unit 351 forgenerating the image based on the collision probability calculated atthe collision probability calculating unit 34, the display unit 352 fordisplay outputting the information including the image generated by theimage generating unit 351, and the warning sound emitting unit 353 foremitting the warning sound (including voice) when the collisionprobability between the subject vehicle course generated by the subjectvehicle-course generating unit 33 and the specific obstacle is largerthan the predetermined threshold value. The configuration of theobstacle course prediction apparatus 41 other than those describedherein is the same as the configuration of the obstacle courseprediction apparatus 11 according to the above-described secondembodiment (refer to FIG. 10).

The obstacle course prediction method according to the fifth embodimentis the same as the obstacle course prediction method according to theabove-described fourth embodiment, except for the details of the courseprediction process of the specific obstacle and the collisionprobability calculation process (refer to the flowchart in FIG. 20).Also, the course prediction process of the specific obstacle is the sameas the obstacle course prediction method according to theabove-described second embodiment (refer to FIGS. 11 and 12). Then, inthe following description, the collision probability calculation process(corresponding to the step S29 in FIG. 20) is described in detail.Meanwhile, in the following description, the same step number is usedfor the same process as the obstacle course prediction method accordingto the above-described fourth embodiment.

FIG. 22 is a flowchart showing a detail of the collision probabilitycalculation process. The collision probability calculation process shownin the drawing is composed of two loop processes and calculates thecollision probability between the trajectory P₀ of the subject vehicleC₀ generated at the step S28 and a non-interference trajectory set{P_(S)′ (n)} remaining unremoved at the step S26. At that time, thecollision probability calculating unit 34 calculates the collisionprobability using the trajectory P₀ of the subject vehicle C₀, thenon-interference trajectory set {P_(S)′ (n)} of the specific obstacleO_(S), and an assessment function, which assesses the collisionprobability between the subject vehicle C₀ and the specific obstacleO_(S). Meanwhile, in the fifth embodiment, although it is describedassuming that the collision probability calculating unit 34 incorporatesthe assessment function, the assessment function may be input fromoutside by providing an input unit to the obstacle course predictionapparatus 41. Also, the assessment function may be adoptively changeddepending on the type of the road and the velocity of the subjectvehicle C₀.

First, the collision probability calculating unit 34 sequentiallyperforms a repetitive process (Loop1) for all the elements P_(S)′(n_(S)) (n_(S)=1, 2, . . . , N_(S)) of the non-interference trajectoryset {P_(S)′ (n)} for the specific obstacle O_(S) (step S501). In thisrepetitive process, an interference degree r_(S) is introduced as anamount to quantitatively provide a degree of interference between thesubject vehicle C₀ and the specific obstacle O_(S), and an initial valueof the interference degree r_(S) is set to 0 (step S502).

Next, the collision probability calculating unit 34 starts therepetitive process (Loop2) to assess the interference between thetrajectory P₀ of the subject vehicle C₀ and one non-interferencetrajectory P_(S)′ (n_(S)) at the specific obstacle O_(S) (step S503). Inthis Loop2, the distance between the two trajectories P₀ and P_(S)′(n_(S)) at the same time is sequentially obtained at the time t=0, Δt, .. . , T. In the fifth embodiment also, when a spatial distance betweenthe two trajectories at the same time becomes smaller than apredetermined value (for example, normal width and length of thevehicle), it is considered that the subject vehicle C₀ and the specificobstacle O_(S) collide with each other, and the maximum value of thedistance with which it is considered that the two vehicles collide witheach other (spatial distance interfering with each other) is referred toas an interference distance.

FIG. 23 is a view schematically showing a relationship in the space-timebetween the trajectory P₀ of the subject vehicle C₀ and thenon-interference trajectory P_(S)′ (n_(S)) of the specific obstacleO_(S). In the shown example, the trajectory P₀ and the non-interferencetrajectory P_(S)′ (n_(S)) intersect with each other at two points a₁ anda₂. Therefore, areas A₁ and A₂ in which a distance between the twotrajectories at the same time is smaller than the interference distanceare present in the vicinity of the two points a₁ and a₂, respectively.That is to say, at the time in which the two trajectories P₀ and P_(S)′(n_(S)) are included in the areas A₁ and A₂, respectively, it isdetermined that the subject vehicle C₀ and the specific obstacle O_(S)collide with each other. In other words, the number to pass through theareas A₁ and A₂ at the time t=0, Δt, . . . , T is the number ofcollisions of the subject vehicle C₀ with the specific obstacle O_(S).

As is clear from FIG. 23, in the space-time environment formed in thefifth embodiment, even when the two trajectories collide with each otheronce the trajectory thereafter is generated. This is because thetrajectories of the objects are separately generated.

After that, the collision probability calculating unit 34 obtains thedistance between the subject vehicle C₀ and the specific obstacle O_(S)and when this determines that the subject vehicle C₀ and the specificobstacle O_(S) collide with each other in the above-described sense as aresult thereof (Yes at the step S504), this defines a value of theinterference degree r_(S) as

[Equation 8]

r_(S)←r_(S)+p(P_(S)′ (n_(S)))  (8)

(step S505). Herein, a second term p(P_(S)′ (n_(S))) is the probabilitythat the trajectory P_(S)′ (n_(S)) is selected (herein, the probabilitydistribution function already provided to each trajectory is normalizedby the normalizing unit 8). Meanwhile, when the subject vehicle C₀ andthe specific obstacle O_(S) do not collide with each other at the stepS504, the procedure directly proceeds to a step S506 to be describedlater.

The collision probability calculating unit 34 does not finish therepetition when the time t does not reach T after the step S505 (No atthe step S506), and increments the value of t by Δt (step S507), andreturns to the step S503 to repeat the Loop2. On the other hand, thecollision probability calculating unit 34 finishes the Loop2 when thetime t reaches T after the step S505 (Yes at the step S506).

By the above-described repetitive process of Loop2, the value of theinterference degree r_(S) becomes larger value as the number ofcollisions is larger. After the Loop2 is finished, the collisionprobability calculating unit 34 performs determination process whetherto repeat the Loop1 or not. That is to say, when there is the trajectoryof which interference assessment with the trajectory P₀ of the subjectvehicle C₀ is not performed out of the trajectories generated for thespecific obstacle O_(S), this does not finish the Loop1 (No at the stepS508) and makes n_(S) to n_(S)+1 (step S509), and returns back to thestep S501 to repeat the Loop1.

On the other hand, when the interference assessment with the trajectoryP₀ of the subject vehicle C₀ is performed for all the trajectoriesP_(S)′ (n_(S)) generated for the specific obstacle O_(S) (Yes at thestep S508), the collision probability calculating unit 34 provides thefinal interference degree r_(S) to assess the interference between thetrajectory P₀ of the subject vehicle C₀ and all the non-interferencetrajectory set {P_(S)′ (n)} of the specific obstacle O_(S) (step S510),and outputs the provided interference degree r_(S) to store in thememory unit 10 (step S511).

Herein, when the number of collisions of the trajectory P₀ of thesubject vehicle C₀ and the trajectory P_(S)′ (n_(S)) of the specificobstacle O_(S) is set to M(n_(S)), the value of the interference degreer_(S) is the sum of the values, which are obtained by multiplying theprobability p(P_(S)′ (n_(S))) for each trajectory P_(S)′ (n_(S)) byM(n_(S)), of all the elements of the trajectory set {P_(S)′ (n_(S))}.

$\begin{matrix}\left\lbrack {{Equation}\mspace{14mu} 9} \right\rbrack & \; \\{r_{s} = {\sum\limits_{n_{s} = 1}^{N_{s}}\; {{M\left( n_{s} \right)}{p\left( {P_{s}^{\prime}\left( n_{s} \right)} \right)}}}} & (9)\end{matrix}$

A sum of a right side of the equation (9) is no more than the collisionprobability that the trajectory P₀ of the subject vehicle C₀ collidewith the trajectory, which the specific obstacle O_(S) may take. That isto say, the collision probability that the subject vehicle C₀ and thespecific obstacle O_(S) collide with each other may be obtained by theequation (9).

After that, the output unit 35 displays information with the displayunit 352 and emits the warning sound from the warning sound emittingunit 353 when the interference degree r_(S), which is the collisionprobability between the subject vehicle C₀ and the specific obstacleO_(S), is larger than a predetermined threshold value (step S30)

According to the above-described fifth embodiment of the presentinvention, even about the course, which may be taken with highprobability when seen on a separate obstacle basis, if this interfereswith another obstacle, the prediction may be performed by taking aneffect thereof into consideration, by predicting the course, which theobstacle may take, based on the position and the internal state of theobstacle, and at the time of the prediction, probabilisticallypredicting a plurality of courses of the specific obstacles selected bya predetermined condition and predicting the course of the generalobstacle other than those, and when there are a plurality of obstacles,lowering the predicted probability of the course of which distance fromthe course, which the general obstacle may take, the same time issmaller than a predetermined value out of a plurality of courses, whichthe specific obstacle may take, and calculating the probability thateach of a plurality of courses of the specific obstacle including thecourse of which predicted probability is lowered, is realized.Therefore, it is possible to appropriately predict the course of theobstacle even under the complicated traffic environment.

Also, according to the fifth embodiment, the course of the dynamicobject may be predicted with high accuracy, generating the variation inposition, which the specific obstacle may take with time, as thetrajectory in space-time composed of time and space, andprobabilistically predicting the course of the specific obstacle byusing the generated trajectory.

Further, according to the fifth embodiment, it becomes possible toappropriately determine safety of the subject vehicle course underrealistically possible circumstance within the practical time period, bycalculating the interference degree quantitatively indicating the degreeof interference between the trajectory, which the specific obstacle maytake, and the trajectory, which the subject vehicle may take in thespace-time, and obtaining the calculated interference degree as thecollision probability.

Meanwhile, it is also possible to further provide the subjectvehicle-course generating unit and the collision probability calculatingunit to the obstacle course prediction apparatus according to theabove-described third embodiment, as a modification of the fifthembodiment. In this case, the collision probability between the subjectvehicle C₀ and all the obstacles in the space-time environment byrepeatedly performing the above-described processes from the step S501to the step S511 for a plurality of obstacles.

Another Embodiment

Although the first to fifth embodiments are described in detail as thebest mode for implementing the present invention, the present inventionis not limited to the five embodiments. For example, it may beconfigured such that the predicted probability of the courses, whichinterfere with each other, is lowered in place of setting the predictedprobability of taking the courses interfering with each other to 0 andremoving the same.

Also, it is possible to equally treat all the obstacles by using theobstacle course prediction method described in the above-described firstand second embodiments. In this case, the specific obstacle selectingunit sequentially selects the obstacle extracted by the obstacleextracting unit based on an optional rule (for example, in an order ofdistance with the subject vehicle from the closest to the farthest) andrepeat performing the course prediction process for each specificobstacle by a loop.

Further, it is possible to apply the present invention to an automateddriving system. In this case, it may be configured such that anoperation signal to operate the subject vehicle is generated in responseto the output of the obstacle course prediction apparatus (the coursepredicted result and the collision probability with the subject vehicle)and the operation signal is transmitted to a predetermined actuatordevice provided on the subject vehicle.

Also, it is possible to arrange a virtual obstacle in addition to thereal obstacle detected by the sensor unit and predicts the course forthe virtual obstacle. More specifically, it is possible to form avirtual model, which behaves undesirably for the subject vehicle, andpredicts the course thereof by arranging the model to a predeterminedposition. By arranging such a virtual model to a position, which is notdetected from the subject vehicle traveling near a blind cross point dueto presence of a screen or the like, it becomes possible to predict therisk of collision or the like with the obstacle, which may jump from thecross point. Meanwhile, information of the virtual model may be storedin the memory unit in advance and arranged on a desired positionaccording to condition setting from a separately provided input unit.

When applying the obstacle course prediction apparatus according to thepresent invention to a region such as the express highway at whichtravel of only vehicles is supposed, it may be configured that travelingcircumstances of the vehicles, which travels close to each other, areexchanged by vehicle-to-vehicle communication, by providingcommunication means for the vehicle-to-vehicle communication to eachvehicle. In such a case, it is possible that each vehicle stores theoperation history in its memory unit, provides the operation selectionprobability for each operation based on the operation history, andtransmits this with the information regarding the operation selectionprobability to another vehicle. Thereby, the accuracy of the courseprediction becomes high, and the risk while traveling may be furthersurely avoided.

In addition, it is possible to use the global positioning system (GPS)as position detecting means. In this case, the positional informationand the move information of the object detected by the sensor unit maybe corrected by referring to the three-dimensional map informationstored in the GPS. Further, it is also possible to alternativelycommunicate the output of the GPS to allow to operate as the sensorunit. In any case, it is possible to realize the course prediction withhigh accuracy by using the GPS, and the reliability of the predictedresult may further be improved.

Meanwhile, the obstacle course prediction apparatus according to thepresent invention may be mounted on the mobile object such as thevehicle other than the four-wheeled vehicle, the person, and a robot.

Also, the obstacle course prediction apparatus according to the presentinvention is not necessarily mounted on the mobile object. For example,if the subject vehicle may utilize the vehicle-to-vehicle communicationand road-to-vehicle communication, the obstacle course predictionapparatus according to the present invention may be formed of the courseinterference assessing system including the subject vehicle, anothervehicle around the subject vehicle, and infrastructure. In this case, itis also possible to perform the course prediction calculation of theobstacle on an infrastructure side, and to specify the subject vehicleas the prediction calculation requesting vehicle, which requires theinfrastructure side the prediction calculation result and receives thesame, and performs the process base on the received predictioncalculation result.

As is clear from the above description, the present invention mayinclude various embodiments not described herein, and various designchanges may be made within the scope of the technical idea specified bythe claims.

INDUSTRIAL APPLICABILITY

As described above, the obstacle course prediction method, theapparatus, and the program according to the present invention ispreferable as the technique to avoid the risk when driving the mobileobject such as the four-wheel vehicle and assuring the safety.

1. An obstacle course prediction method, which a computer predicts acourse of the obstacle, the computer providing a memory unit thatmemorizes at least a position and an internal state of an obstaclepresent within a predetermined area from a mobile object, the methodcomprising: performing prediction of a course, which the obstacle maytake, based on the position and the internal state of the obstacle readfrom the memory unit, and performing probabilistic prediction of aplurality of courses, which the obstacle may take, for at least one ofthe obstacles at the time of the prediction; obtaining a course in whichthe obstacles interfere with each other within the courses, which aplurality of the obstacles predicted at the course predicting may take,and lowering predicted probability of the course for which theprobabilistic prediction is performed within the interfering courses,when there are a plurality of the obstacles; and calculating probabilityof realizing each of the plurality of courses including the course inwhich the predicted probability is lowered at the course interferenceassessing.
 2. The obstacle course prediction method according to claim,further comprising; selecting step selecting one of the obstaclessatisfying a predetermined condition as a specific obstacle, wherein thecourse predicting includes performing probabilistic prediction of aplurality of courses, which the specific obstacle may take, andperforming prediction of the course, which the obstacle other than thespecific obstacle may take, the course interference assessingstep-lowers predicted probability of a course of which distance at thesame time from a course, which the general obstacle may take, is smallerthan a predetermined value, within a plurality of courses, which thespecific obstacle may take, when there are a plurality of the obstacles,and the probability calculating calculates probability of realizing eachof a plurality of courses of the specific obstacle including the coursethe predicted probability of which is lowered at the course interferenceassessing.
 3. The obstacle course prediction method according to claim2, wherein the specific obstacle course predicting includes generatingvariation in position, which the specific obstacle may take with time,as a trajectory in space-time composed of time and space, based on theposition and the internal state of the obstacle, and performingprobabilistic prediction calculation of a course, which the specificobstacle may take, using the trajectory generated at the trajectorygenerating.
 4. The obstacle course prediction method according to claim2, wherein the general obstacle course predicting predicts the course ofthe general obstacle assuming that the internal state of the generalobstacle is maintained.
 5. The obstacle course prediction methodaccording to claim 2, further comprising: generating a course of themobile object based on a position and an internal state of the mobileobject; and calculating collision probability between the course of themobile object generated at the course generating and each of a pluralityof courses, which the specific obstacle may take, of which probabilityis calculated for each course at the probability calculating.
 6. Theobstacle course prediction method according to claim 1, wherein thecourse predicting performs probabilistic prediction of a plurality ofcourses, which the obstacle may take, the course interference assessinglowers probability of taking the course of which distance at the sametime from the course of another of the obstacles is smaller than apredetermined value within the courses, which a plurality of theobstacles may take, when there a plurality of the obstacles, and theprobability calculating calculates the probability of realizing each ofall the courses of a plurality of the obstacles including the course thepredicted probability of which is lowered at the course interferenceassessing.
 7. The obstacle course prediction method according to claim6, wherein the course predicting includes generating variation inposition, which the obstacle may take with time, as a trajectory in aspace-time composed of time and space, based on the position and theinternal state of the obstacle, and performing probabilistic predictioncalculation of the course of the obstacle by using the trajectorygenerated at the trajectory generating.
 8. The obstacle courseprediction method according to claim 6, further comprising: generating acourse of the mobile object based on the position and the internal stateof the mobile object; and calculating collision probability between thecourse of the mobile object generated at the course generating and eachof all courses, which the obstacle may take, probability of which iscalculate for each course at the probability calculating.
 9. An obstaclecourse prediction apparatus that predicts a course of an obstacleexisting around a mobile object, comprising: a memory unit thatmemorizes at least a position and an internal state of the obstaclepresent within a predetermined area from the mobile object; a coursepredicting unit that performs prediction of a course, which the obstaclemay take, based on the position and the internal state of the obstacleread from the memory unit, and performs probabilistic prediction of aplurality of courses, which the obstacle may take, for at least one ofthe obstacles at the time of the prediction; a course interferenceassessing unit that obtains a course in which the obstacles interferewith each other within the courses, which a plurality of the obstaclespredicted at the course predicting unit may take, and lowers predictedprobability of the course for which the probabilistic prediction isperformed within the interfering courses, when there are a plurality ofthe obstacles; and a probability calculating unit that calculatesprobability of realizing each of the plurality of courses including thecourse in which the predicted probability is lowered at the courseinterference assessing unit.
 10. The obstacle course predictionapparatus according to claim 9, further comprising: a specific obstacleselecting unit that selects one of the obstacles satisfying apredetermined condition as a specific obstacle, wherein the coursepredicting unit has a specific obstacle course predicting unit thatperforms probabilistic prediction of a plurality of courses, which thespecific obstacle may take, and a general obstacle course predictingunit that performs prediction of the course, which the obstacle otherthan the specific obstacle may take, the course interference assessingunit lowers predicted probability of a course of which distance at thesame time from a course, which the general obstacle may take, is smallerthan a predetermined value, within a plurality of courses, which thespecific obstacle may take, when there are a plurality of the obstacles,and the probability calculating unit calculates probability of realizingeach of a plurality of courses of the specific obstacle including thecourse the predicted probability of which is lowered at the courseinterference assessing unit.
 11. The obstacle course predictionapparatus according to claim 10, wherein the specific obstacle coursepredicting unit includes a trajectory generating unit that generatesvariation in position, which the specific obstacle may take with time,as a trajectory in space-time composed of time and space, based on theposition and the internal state of the obstacle, and a predictioncalculating unit that performs probabilistic prediction calculation of acourse, which the specific obstacle may take, using the trajectorygenerated at the trajectory generating unit.
 12. The obstacle courseprediction apparatus according to claim 10, wherein the general obstaclecourse predicting unit predicts the course of the general obstacleassuming that the internal state of the general obstacle is maintained.13. The obstacle course prediction apparatus according to claim 10,further comprising: a course generating unit that generates a course ofthe mobile object based on a position and an internal state of themobile object; and a collision probability calculating unit thatcalculates collision probability between the course of the mobile objectgenerated at the course generating unit and each of a plurality ofcourses, which the specific obstacle may take, of which probability iscalculated for each course at the probability calculating unit.
 14. Theobstacle course prediction apparatus according to claim 9, wherein thecourse predicting unit performs probabilistic prediction of a pluralityof courses, which the obstacle may take, the course interferenceassessing unit lowers probability of taking the course of which distanceat the same time from the course of another of the obstacles is smallerthan a predetermined value within the courses, which a plurality of theobstacles may take, when there are a plurality of the obstacles, and theprobability calculating unit calculates the probability of realizingeach of all the courses of a plurality of the obstacles including thecourse the predicted probability of which is lowered at the courseinterference assessing unit.
 15. The obstacle course predictionapparatus according to claim 14, wherein the course prediction unitincludes a trajectory generating unit that generates variation inposition, which the obstacle may take with time, as a trajectory in aspace-time composed of time and space, based on the position and theinternal state of the obstacle, and a prediction calculating unit thatperforms probabilistic prediction calculation of the course of theobstacle by using the trajectory generated at the trajectory generatingunit.
 15. The obstacle course prediction apparatus according to claim14, further comprising: a course generating unit that generates a courseof the mobile object based on the position and the internal state of themobile object; and a collision probability calculating unit thatcalculates collision probability between the course of the mobile objectgenerated at the course generating unit and each of all courses, whichthe obstacle may take, probability of which is calculate for each courseat the probability calculating unit.
 17. An obstacle course predictionprogram for allowing the computer to execute the obstacle courseprediction method according to claim 1.